Healthcare Data Quality Assessment for Improving the Quality of the Korea Biobank Network

Overview

Journal PLoS One

Specialties General Medicine
Science

Date 2023 Nov 20

PMID 37983215

Authors

Ki-Hoon Kim

Seol Whan Oh

Soo Jeong Ko

Kang Hyuck Lee

Wona Choi

In Young Choi

Affiliations

Soon will be listed here.

Abstract

Numerous studies make extensive use of healthcare data, including human materials and clinical information, and acknowledge its significance. However, limitations in data collection methods can impact the quality of healthcare data obtained from multiple institutions. In order to secure high-quality data related to human materials, research focused on data quality is necessary. This study validated the quality of data collected in 2020 from 16 institutions constituting the Korea Biobank Network using 104 validation rules. The validation rules were developed based on the DQ4HEALTH model and were divided into four dimensions: completeness, validity, accuracy, and uniqueness. Korea Biobank Network collects and manages human materials and clinical information from multiple biobanks, and is in the process of developing a common data model for data integration. The results of the data quality verification revealed an error rate of 0.74%. Furthermore, an analysis of the data from each institution was performed to examine the relationship between the institution's characteristics and error count. The results from a chi-square test indicated that there was an independent correlation between each institution and its error count. To confirm this correlation between error counts and the characteristics of each institution, a correlation analysis was conducted. The results, shown in a graph, revealed the relationship between factors that had high correlation coefficients and the error count. The findings suggest that the data quality was impacted by biases in the evaluation system, including the institution's IT environment, infrastructure, and the number of collected samples. These results highlight the need to consider the scalability of research quality when evaluating clinical epidemiological information linked to human materials in future validation studies of data quality.

References

Weiskopf N, Weng C . Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. J Am Med Inform Assoc. 2012; 20(1):144-51. PMC: 3555312. DOI: 10.1136/amiajnl-2011-000681. View

Carter A, Betsou F . Quality assurance in cancer biobanking. Biopreserv Biobank. 2014; 9(2):157-63. DOI: 10.1089/bio.2010.0031. View

AlKaabi L, Ahmed L, Al Attiyah M, Abdel-Rahman M . Predicting hypertension using machine learning: Findings from Qatar Biobank Study. PLoS One. 2020; 15(10):e0240370. PMC: 7567367. DOI: 10.1371/journal.pone.0240370. View

Puttkammer N, Baseman J, Devine E, Valles J, Hyppolite N, Garilus F . An assessment of data quality in a multi-site electronic medical record system in Haiti. Int J Med Inform. 2015; 86:104-16. DOI: 10.1016/j.ijmedinf.2015.11.003. View

Biedermann P, Ong R, Davydov A, Orlova A, Solovyev P, Sun H . Standardizing registry data to the OMOP Common Data Model: experience from three pulmonary hypertension databases. BMC Med Res Methodol. 2021; 21(1):238. PMC: 8565035. DOI: 10.1186/s12874-021-01434-3. View

Bellos I, Pergialiotis V, Perrea D . Kidney biopsy findings in vancomycin-induced acute kidney injury: a pooled analysis. Int Urol Nephrol. 2021; 54(1):137-148. DOI: 10.1007/s11255-021-02831-9. View

Lin E, Kuo P, Lin W, Liu Y, Yang A, Tsai S . Prediction of Probable Major Depressive Disorder in the Taiwan Biobank: An Integrated Machine Learning and Genome-Wide Analysis Approach. J Pers Med. 2021; 11(7). PMC: 8308113. DOI: 10.3390/jpm11070597. View

Blacketer C, Defalco F, Ryan P, Rijnbeek P . Increasing trust in real-world evidence through evaluation of observational data quality. J Am Med Inform Assoc. 2021; 28(10):2251-2257. PMC: 8449628. DOI: 10.1093/jamia/ocab132. View

Mecatti G, Sanchez-Vinces S, Fernandes A, Messias M, de Santis G, M Porcari A . Potential Lipid Signatures for Diagnosis and Prognosis of Sepsis and Systemic Inflammatory Response Syndrome. Metabolites. 2020; 10(9). PMC: 7570015. DOI: 10.3390/metabo10090359. View

10.

Lynch K, Deppen S, DuVall S, Viernes B, Cao A, Park D . Incrementally Transforming Electronic Medical Records into the Observational Medical Outcomes Partnership Common Data Model: A Multidimensional Quality Assurance Approach. Appl Clin Inform. 2019; 10(5):794-803. PMC: 6811349. DOI: 10.1055/s-0039-1697598. View

11.

Schober P, Boer C, Schwarte L . Correlation Coefficients: Appropriate Use and Interpretation. Anesth Analg. 2018; 126(5):1763-1768. DOI: 10.1213/ANE.0000000000002864. View

12.

Parkin D, Bray F . Evaluation of data quality in the cancer registry: principles and methods Part II. Completeness. Eur J Cancer. 2009; 45(5):756-64. DOI: 10.1016/j.ejca.2008.11.033. View

13.

Zhang X, Du L, Qiao Y, Zhang X, Zheng W, Wu Q . Ferroptosis is governed by differential regulation of transcription in liver cancer. Redox Biol. 2019; 24:101211. PMC: 6526247. DOI: 10.1016/j.redox.2019.101211. View

14.

Huser V, Defalco F, Schuemie M, Ryan P, Shang N, Velez M . Multisite Evaluation of a Data Quality Tool for Patient-Level Clinical Data Sets. EGEMS (Wash DC). 2017; 4(1):1239. PMC: 5226382. DOI: 10.13063/2327-9214.1239. View

15.

Byun J, Choi Y, Kang Y, Jang B, Kang K, Jeon Y . Retinoic acid-related orphan receptor alpha reprograms glucose metabolism in glutamine-deficient hepatoma cells. Hepatology. 2014; 61(3):953-64. DOI: 10.1002/hep.27577. View

16.

Norlin L, Fransson M, Eriksson M, Merino-Martinez R, Anderberg M, Kurtovic S . A Minimum Data Set for Sharing Biobank Samples, Information, and Data: MIABIS. Biopreserv Biobank. 2014; 10(4):343-8. DOI: 10.1089/bio.2012.0003. View

17.

Park O, Cho S, Shin S, Park J, Kim J, Han B . A strategic plan for the second phase (2013-2015) of the Korea biobank project. Osong Public Health Res Perspect. 2013; 4(2):107-16. PMC: 3767092. DOI: 10.1016/j.phrp.2013.03.006. View

18.

Sanson-Fisher R, Bonevski B, Green L, DEste C . Limitations of the randomized controlled trial in evaluating population-based health interventions. Am J Prev Med. 2007; 33(2):155-61. DOI: 10.1016/j.amepre.2007.04.007. View

19.

Mukaka M . Statistics corner: A guide to appropriate use of correlation coefficient in medical research. Malawi Med J. 2013; 24(3):69-71. PMC: 3576830. View

20.

Ferdyn K, Glenska-Olender J, Witon M, Zagorska K, Kozera L, Chroscicka A . Quality Management System in the BBMRI.pl Consortium: Status Before the Formation of the Polish Biobanking Network. Biopreserv Biobank. 2019; 17(5):401-409. DOI: 10.1089/bio.2018.0127. View